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    <title>Pinboard (Vaguery)</title>
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    <description>recent bookmarks from Vaguery</description>
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	<rdf:li rdf:resource="http://arxiv.org/abs/0812.3141"/>
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  </channel><item rdf:about="http://arxiv.org/abs/1310.5103">
    <title>[1310.5103] Threshold-free Evaluation of Medical Tests for Classification and Prediction: Average Precision versus Area Under the ROC Curve</title>
    <dc:date>2013-12-11T15:04:30+00:00</dc:date>
    <link>http://arxiv.org/abs/1310.5103</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[When evaluating medical tests or biomarkers for disease classification, the area under the receiver-operating characteristic (ROC) curve is a widely used performance metric that does not require us to commit to a specific decision threshold. For the same type of evaluations, a different metric known as the average precision (AP) is used much more widely in the information retrieval literature. We study both metrics in some depths in order to elucidate their difference and relationship. More specifically, we explain mathematically why the AP may be more appropriate if the earlier part of the ROC curve is of interest. We also address practical matters, deriving an expression for the asymptotic variance of the AP, as well as providing real-world examples concerning the evaluation of protein biomarkers for prostate cancer and the assessment of digital versus film mammography for breast cancer screening.
]]></description>
<dc:subject>bioinformatics statistics performance-measure statistical-tests philosophy-of-engineering</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4dccfb0071ce/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bioinformatics"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistical-tests"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-engineering"/>
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<item rdf:about="http://arxiv.org/abs/0812.3141">
    <title>[0812.3141] Choosing a penalty for model selection in heteroscedastic regression</title>
    <dc:date>2010-06-19T12:44:20+00:00</dc:date>
    <link>http://arxiv.org/abs/0812.3141</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["We consider the problem of choosing between several models in least-squares regression with heteroscedastic data. We prove that any penalization procedure is suboptimal when the penalty is a function of the dimension of the model, at least for some typical heteroscedastic model selection problems. In particular, Mallows' Cp is suboptimal in this framework. On the contrary, optimal model selection is possible with data-driven penalties such as resampling or $V$-fold penalties. Therefore, it is worth estimating the shape of the penalty from data, even at the price of a higher computational cost. Simulation experiments illustrate the existence of a trade-off between statistical accuracy and computational complexity. As a conclusion, we sketch some rules for choosing a penalty in least-squares regression, depending on what is known about possible variations of the noise-level."
]]></description>
<dc:subject>statistics statistical-tests linear-regression meta-optimization nudge-targets multiobjective-optimization pragmatism-it-ain't</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:a2085473faa6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pragmatism-it-ain't"/>
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<item rdf:about="http://arxiv.org/abs/1005.4358">
    <title>[1005.4358] On the estimation of the extremal index based on scaling and resampling</title>
    <dc:date>2010-05-26T11:03:57+00:00</dc:date>
    <link>http://arxiv.org/abs/1005.4358</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["The extremal index parameter theta characterizes the degree of local dependence in the extremes of a stationary time series and has important applications in a number of areas, such as hydrology, telecommunications, finance and environmental studies.…Further, a procedure for the automatic selection of its tuning parameter is developed and different types of confidence intervals that prove useful in practice proposed. The performance of the estimator is examined through simulations, which show its highly competitive behavior. Finally, the estimator is applied to three real data sets of daily crude oil prices, daily returns of the S&P 500 stock index, and high-frequency, intra-day traded volumes of a stock. These applications demonstrate additional diagnostic features of statistical plots based on the new estimator."
]]></description>
<dc:subject>statistics time-series statistical-tests nudge-targets algorithms extreme-values</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:54aaafe02582/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
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<item rdf:about="http://arxiv.org/abs/1003.2294">
    <title>[1003.2294] A Simple Lack-of-Fit Test for Regression Models</title>
    <dc:date>2010-04-30T21:30:59+00:00</dc:date>
    <link>http://arxiv.org/abs/1003.2294</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["A simple test is proposed for examining the correctness of a given completely specified response function against unspecified general alternatives in the context of univariate regression. The usual diagnostic tools based on residuals plots are useful but heuristic. We introduce a formal statistical test supplementing the graphical analysis. Technically, the test statistic is the maximum length of the sequences of ordered (with respect to the covariate) observations that are consecutively overestimated or underestimated by the candidate regression function. Note that the testing procedure can cope with heteroscedastic errors and no replicates. Recursive formulae allowing to calculate the exact distribution of the test statistic under the null hypothesis and under a class of alternative hypotheses are given."
]]></description>
<dc:subject>statistics statistical-tests modeling algorithms things-to-ask-Cosma-about</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b066dc61daf4/</dc:identifier>
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